In the first funding phase, the Modeling Initiative constructed a number of relational and biophysical models of mechanics and molecular phenomena related to cell migration and started to develop migration related capabilities within the Virtual Cell (VC) software. This activity can be considered as the last step in the reductionism agenda - in silico reconstitution of a simplified motile system using mathematical representation combining biological knowledge and hypotheses, with determination of the consequences of these hypotheses facilitated beyond human reasoning by means of computer-generated numerical calculations. These models and software development enabled the exciting possibility to make a large, critical step in our quantitative understanding of cell migration from the point of view of systems biology. The models will be standardized from the technical point of view, integrated, comprehensive and predictive. A crucial feature of our endeavor, absolutely required for validating such models and using them for hypothesis prediction-test efforts, is that no modeling is undertaken absent direct input from experimental collaborators. We will describe below the mechanism by which this requirement will be consistently met. The Modeling Initiative will investigate migration mechanisms at the systems-level with a long term goal of developing a comprehensive model of cell migration. This model will have a modular character combining deterministic and stochastic components. Our approach is to develop models for each of the component processes that drive cell migration, e.g., development of polarity, protrusion, adhesion, and contraction and rear release, and then integrate them into a comprehensive model. For each of these processes, a 'Process Team'that includes both computational biologists and experimental biologists (in a few cases, these capabilities reside within the same laboratory ), will work together to develop a 'Process Model'capable of capturing dynamic behavior in terms of molecular properties (protein levels, states, locations, and activities). It is through this collaborative team that data will be produced, analyzed, and modeled iteratively with a goal of developing additional data from model predictions and using these data to refine the models.